Databricks & Apache Spark Programme Guide
Explore distributed data processing, notebooks and lakehouse workflows, with attention to the difference between a processing engine and a managed platform.
Programme enquiry: ask the team to confirm availability, syllabus, trainer, fees and batch arrangements before enrolling.
What this field involves
Explore distributed data processing, notebooks and lakehouse workflows, with attention to the difference between a processing engine and a managed platform.
- Spark DataFrames and transformations
- Partitions, joins and execution concepts
- Delta tables and lakehouse workflows
- Jobs, data quality and access controls
Useful foundations
Python or SQL, database basics and familiarity with data pipelines are helpful.
A practical question to investigate
Outline a pipeline that cleans event data, joins a reference dataset and produces an analytical table. Describe how you would inspect failures and avoid duplicate processing.
This is an illustrative learning brief, not a promise about an SV Infotech batch or its project assignments.
Before choosing a programme
Are Spark and Databricks the same thing?
Apache Spark is a distributed processing engine. Databricks provides a managed platform with integrated tools and services around data and AI workflows. Understand the core processing concepts as well as the platform-specific experience.
Can I join a Databricks & Apache Spark batch at SV Infotech?
Contact the team to confirm whether this programme is currently offered, its entry requirements and the available training arrangements. Fees, duration, trainer and batch dates are provided after confirmation.
Review the official learning or product documentation to understand the technology. Product references do not imply vendor affiliation, accreditation or certification.